Deep Learning-Driven System for Automated Identification of Plant Nutrient Deficiencies

Modern plant health is critical in agricultural practices as it determines the yield on crops needed for nutritional requirement. Lack of nutrients in plants affects crops in different ways and the productivity of agriculture consequently decreases. To tackle this, we present a new approach for plant health monitoring through deep learning with computer vision approach. The core of this research is based on creating a smart system for nutrient deficiency detection on plants. It involves image processing for extracting the features and patterns of the plant leaves and then classify the nutrient deficiencies to nitrogen, phosphorus, and potassium among others. Its chief advantage is that the system provides instantaneous, precise diagnoses. The flexibility is achieved with the help of transfer learning that can be applied to different kinds of crops and different conditions. Envelopment of the proposed system is high-level and costeffective compared to current methods that enable early diagnostics of nutrient deficiencies in crops and provide farmers with details regarding their crops' condition. This technology can help avoid many manual inspections, help improve the management practices of crops as well as minimize overall yield losses and optimum use of fertilizer leading to more sustainable agriculture. Our model adopts transfer learning-based Convolutional Neural Network (CNN) and the identification accuracy of the model reaches 93.12 %, which also proves that high accuracy is a reliable signal for plant disease identification. This results into accurate and timely diagnoses by the system reducing reliance on manual inspections as well as offering equity to farmers with diagnosis results. This innovation applies sustainable agricultural practices in that it accurately and early diagnoses plant diseases.